Keyframe selection facilitates SLAM systems to be more efficient. In the key-frame based SLAM systems, pose and scene estimates are only refined when a keyframe is detected. [1]} provides a learning solution to detect key-frames together with unsupervised learning of ego-motion tracking and depths estimation [2]}. Whether an image is the keyframe is determined by comparing its feature similarity with existing keyframes (i.e. if the similarity is below a threshold, this image will be treated as a new keyframe).
